[Insight-users] Registering US -> CT

N.E. Mackenzie Mackay 9nem at qlink.queensu.ca
Fri Sep 24 14:47:35 EDT 2004 

Hi Luis,

	On a side note I took your advice and looked at the online tutorials.  
There is a section in the "clinical applications" tutorial that is 
accomplishing the exact same as I want to do.  I am refering to the 
Ultrasound to liver model registration.  They describe a process of 
registering a model of liver tubes to a single ultrasound image.  I am 
trying to do the same thing but with a bone as my model.  Is this code 
that was used in the example of the tutorial available for public use?

Thanks,
Neilson
On Sep 23, 2004, at 6:22 PM, Luis Ibanez wrote:

>
>
> Hi Neilson,
>
> Actually, there is another option.
>
> You could calculate 2D derivatives in the Ultrasound Images,
> take the 2D vectors and map them into their real position and
> orientation in 3D space by taking into account the spatial
> position and orientation of the US slice.
>
> You still need to define a width on the US slice in order to
> determine which points from the 3D CT are considered to be
> interacting with the US slice.
>
> In this way you simply use a GradientRecursiveGaussianImageFilter
> instantiated in 2D for computing the gradients from the US.
>
>
>
>    Regards,
>
>
>       Luis
>
>
>
> ----------------------------
> N.E. Mackenzie Mackay wrote:
>
>> Hey Luis,
>>     If I did do this would this be the only way to actually calculate 
>> the derivative?  I was looking through the 
>> itkNormalizedCorrelationPointSetToImageMetric and it looks like the 
>> points that are on the image are the only ones taken into account 
>> when calculating the gradient.
>>     In my original code with Mutual information I had the Ultrasound 
>> image as the fixed image so that the gradient would be calculated 
>> using the 3D CT.  For any PointSetToImageMetric I can't set the US 
>> image as the fixed image.  Making the gradient only calculated using 
>> the 2D US.  Even if I applied the blurring would this make it 
>> possible for the Gradient to be calculated properly?
>> Thanks,
>> Neilson
>> On Sep 19, 2004, at 7:51 PM, Luis Ibanez wrote:
>>>
>>> Hi Neilson,
>>>
>>> No,
>>> if you read a 2D image and you apply a 3D blurring, what you
>>> will get is an Exception thrown. A 2D image is considered
>>> to be a degenerate 3D image and therefore it is not suitable
>>> for being the input of a 3D filter.
>>>
>>> What you can do, however, is to read the 2D image, declare
>>> a 3D image with thickness 5 pixels (or 2N+1...)  copy the
>>> slice that you read into the middle slice of the 2N+1
>>> image.  Then, apply a blurring filter.
>>>
>>> You can set the pixel spacing Dz in this new 3D image in such
>>> a way that Dz * (2N+1) = the physical thicknes of the sensitive
>>> plane of your Ultra Sound acquisition probe.
>>>
>>> In that way this new 3D image will be a better representation
>>> of the physical reality that your Ultra Sound 2D image captured.
>>>
>>>
>>>   Regards,
>>>
>>>
>>>     Luis
>>>
>>>
>>> ---------------------------------
>>> N.E. Mackenzie Mackay wrote:
>>>
>>>> That sounds like a good idea.
>>>> If I read in the 2d image as 3D and applied a blurring filter would 
>>>> that cause a 3D blurring ( blur the pixels outside of the plane )?  
>>>> If so, I could set the "thickness" by the setting the radius of the 
>>>> blurring mask.
>>>> Just a thought.
>>>> Neilson
>>>> On Sep 16, 2004, at 6:47 PM, Luis Ibanez wrote:
>>>>
>>>>>
>>>>> Hi Neilson,
>>>>>
>>>>> That's a good point. You are right,
>>>>> in this case, since you have scattered slices
>>>>> from Ultra Sound it is not possible to make sure
>>>>> that every point will be inside an image.
>>>>>
>>>>> One option could be to associate a "thickness"
>>>>> to every US slice, you can probably figure out
>>>>> one that makes sense from the point of view of
>>>>> the physical acquisicion process.
>>>>>
>>>>> That thickness could be used for defining
>>>>> regions of space where a point will be considered
>>>>> to be "inside" one of the US images.
>>>>>
>>>>> The more US image you have, the more chances
>>>>> there are that this could lead to a reasonable
>>>>> registration.
>>>>>
>>>>> Note that this requires you to do more modifications
>>>>> on the ImageMetric class.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>    Regards,
>>>>>
>>>>>
>>>>>       Luis
>>>>>
>>>>>
>>>>>
>>>>> --------------------------------
>>>>> N.E. Mackenzie Mackay wrote:
>>>>>
>>>>>> I was thinking the same thing.
>>>>>> The only thing I was worried about is using the method in 3D.  If 
>>>>>> some of the points don't map onto the the US image will the 
>>>>>> registration method ignore those points or will it throw an 
>>>>>> error?
>>>>>> On Sep 14, 2004, at 10:33 PM, Luis Ibanez wrote:
>>>>>>
>>>>>>>
>>>>>>> Hi Neilson,
>>>>>>>
>>>>>>> MutualInformation is mostly a region-based image metric.
>>>>>>> This means that its value gets better when the overlap
>>>>>>> between matching regions of the image is large. Mutual
>>>>>>> Information is not particularly well suited for matching
>>>>>>> thin structures since thir random sampling is unlikely
>>>>>>> to select many pixels belonging to those structures.
>>>>>>>
>>>>>>> In that sense you probably shouldn't expect much from Mutual
>>>>>>> Information for registering Bone, since bone structures are
>>>>>>> mostly shell-like and they don't fill large regions of space.
>>>>>>> E.g. large bones have their layers of cortical bone with high
>>>>>>> calcifications but their width usually cover just a couple of
>>>>>>> pixels in a CT scan.
>>>>>>>
>>>>>>> Given that you seem to have segmented the bone from the CT scan,
>>>>>>> it is probably worth to try a Model-to-Image registration 
>>>>>>> approach.
>>>>>>> This can be done by taking points on the surface of your bone
>>>>>>> segmentation, and/or from a band around that surface, and using
>>>>>>> them to match the intensities (and structure) of the same bone as
>>>>>>> seen in the UltraSound images.
>>>>>>>
>>>>>>> Could you post a couple of the US images ?
>>>>>>>
>>>>>>> (e.g. you could put them in www.mypacs.net and let us know their
>>>>>>>  image ID).
>>>>>>>
>>>>>>>
>>>>>>> Depending on how the bone structures look like on the US image
>>>>>>> there may be different possible metrics to try in a PointSet to
>>>>>>> Image registration.
>>>>>>>
>>>>>>>
>>>>>>> BTW, when you start working in 3D, don't attempt to use Rigid
>>>>>>> transforms until you have manage to tune all the other parameters
>>>>>>> of the registration to work with simple translation transforms.
>>>>>>> It is more effective to deal with a single issue at a time.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>>
>>>>>>>     Luis
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> -------------------------------
>>>>>>> N.E. Mackenzie Mackay wrote:
>>>>>>>
>>>>>>>> Hi,
>>>>>>>>     I have tried for the last while to get a single ultrasound 
>>>>>>>> image to register to a CT volume.  Specifically try to get the 
>>>>>>>> bone of an ultrasound image and bone of the CT to register 
>>>>>>>> together.  Up to now I am having quite some trouble.
>>>>>>>> I have been able to segment the bone from CT and give an 
>>>>>>>> estimate on where the bone is in the ultrasound.  I am now 
>>>>>>>> trying to register those two images.
>>>>>>>> This is what I am using:
>>>>>>>> MattesMutualInformationImageToImageMetric - decided to use this 
>>>>>>>> because the registration was of two different modalities.  
>>>>>>>> Couldn't use feature registration becuase to hard to segment 
>>>>>>>> ultrasound correctly
>>>>>>>>     - using 50 bins and %20-%100 of samples still doesn't give 
>>>>>>>> adequate results.
>>>>>>>> linearInterpolateImageFunction
>>>>>>>> RegularSetGradientDecentOptimizer
>>>>>>>> Euler3DTransform
>>>>>>>> Both images ( 3D CT and US ) are normalized before the 
>>>>>>>> registration.
>>>>>>>> I have used a maximum step ranging from 0.5-6.  And a min step 
>>>>>>>> of 0.005 - 1.
>>>>>>>> I have a good initial guess ( maximum 2cm away from correct 
>>>>>>>> with about 0- 30degrees of rotation)> I tested out the 
>>>>>>>> registration method in 2D and have had success.  When I use the 
>>>>>>>> exact same variables applied in 3D the registration is poor.
>>>>>>>> Does anyone have any suggestions?  I would be happy to provide 
>>>>>>>> a couple of images or actual code to show you what I am dealing 
>>>>>>>> with.
>>>>>>>> Neilson
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>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
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